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It would be useful to create a simplified version of the groundwater deviation maps combining the (interpreted) deviation for mean highest (mhw_x) and mean lowest groundwater levels (mlw_x) in a few descriptive categories for each vegetation type x.
For each vegetation type, there would be one extra raster then with all the (theoretically) possible combinations of the qualitative classification of the mean highest and lowest groundwater levels. The legend would be:
mhw
mlw
suitable
suitable
suitable
too wet
suitable
too dry
too wet
suitable
too wet
too wet
too wet
too dry
too dry
suitable
too dry
too wet
too dry
too dry
Suitable means within the range (min-max) provided in the NICHE reference table, and is reflected by a value 0 in the respective deviations maps. Too wet means a negative, and too dry a positive value in the respective deviation maps.
The resulting raster could probably be created and exported automatically along with the deviation maps (to avoid having to use another option). deviation=True would then create the 28*2 (quantitative) deviation maps + an extra set of 28 maps with the result of the (qualitative) combinations of the classified versions of these rasters for each vegetation type.
The (color) coding for the different combinations should be constant even if some categories are missing.
The text was updated successfully, but these errors were encountered:
Niche Python Package
It would be useful to create a simplified version of the groundwater deviation maps combining the (interpreted) deviation for mean highest (mhw_x) and mean lowest groundwater levels (mlw_x) in a few descriptive categories for each vegetation type x.
For each vegetation type, there would be one extra raster then with all the (theoretically) possible combinations of the qualitative classification of the mean highest and lowest groundwater levels. The legend would be:
Suitable means within the range (min-max) provided in the NICHE reference table, and is reflected by a value 0 in the respective deviations maps. Too wet means a negative, and too dry a positive value in the respective deviation maps.
The resulting raster could probably be created and exported automatically along with the deviation maps (to avoid having to use another option). deviation=True would then create the 28*2 (quantitative) deviation maps + an extra set of 28 maps with the result of the (qualitative) combinations of the classified versions of these rasters for each vegetation type.
The (color) coding for the different combinations should be constant even if some categories are missing.
The text was updated successfully, but these errors were encountered: